(1) Field of the Invention
This invention relates to a method and system for measuring degradations of a video image introduced by a coding system with reduction in throughput.
It is particularly but not exclusively applicable to the domain of low throughput or very low throughput digital audiovisual signal distribution networks, and to the domain of production of such signals. It is particularly applicable to surveillance of the service quality of a digital audiovisual signal broadcast network.
(2) Prior Art
Digitizing of video signals provides a means for copying, storing and transmitting this type of information while maintaining a constant image quality. However, in practice, the large quantity of information transferred by video images requires the use of digital compression methods to reduce the binary throughput.
A compression method that is very widely used in video is described in standard ISO/CEI 13918 MPEG2. This algorithm is said to be of the “with losses” type since the restored image after decoding is not identical to the original. This algorithm is based on a division of the image into blocks and application of a transform, for example of the discrete cosine transform type, to the pixels in each block to obtain a frequency representation of the luminance amplitude of pixels in the form of one coefficient for each pixel in the block.
In order to maintain an acceptable quality for the final television viewer, the compression algorithms take into account perception properties of the human vision system. However, throughput constraints imposed by transmission systems require the application of compression ratios that have an influence on the image quality perceived by the television viewer.
It is found that the importance of degradations caused by coding depends both on the compression ratio and the complexity of images. These degradations are particularly important when the image is more complex, and particularly related to movement of objects, brightness and texture.
The degradations that appear in the images following application of the MPEG2 coding technique include granular errors, deformations of contours, the appearance of so-called “exotic” contours and block effects.
Therefore it would appear necessary to continuously evaluate the quality of broadcast images. There are widely used subjective evaluation methods for this purpose, that make use of human evaluation. However, these methods are difficult to use and cannot be used on a broadcasting network in operation in real time.
There are other so-called “with reference” methods based on comparison of the image for which the quality is to be evaluated with a reference image. The reference image is usually an image that corresponds to the image to be analyzed before it is coded and/or transmitted. This solution is not very practical, because it requires access to one or several reference images. Furthermore if the video image is transmitted, there is also the problem of transporting the reference image to the place at which the image to be analyzed is received.
Other so-called “without reference” solutions are used to automatically analyze images without needing to make a comparison with reference images. The efficiency and robustness of each of these solutions lies in the method used to measure parameters related to image quality.
Some of these solutions are based on detection of the block effect made in the spatial domain by gradient calculations at block boundaries. To avoid confusion between the boundary of an object in the image with a block effect, the gradient is compared with intra-block gradients. The block effect is detected by means of a decision criterion applied to the behaviour of inter block and intra block gradients.
Thus, the Rohde & Schwarz Company has developed a block effect detection method consisting of calculating a horizontal gradient vector for each macro block in the image, and calculating an average of each component of the vector over the entire image. Variations of components of this vector over time are used to bring out components with marginal behaviour that represent block boundaries degraded by the compression processing transform. Detection of these marginal components provides a means for determining a block effect detection criterion representative of the image degradation.
This principle for calculating the gradient is also described in patent FR 2 805 429 filed by the Applicant. This patent application describes a method based on the combination of a binary gradient image and a movement “pseudo vectors” image calculated from at least two successive images. A combination of these two images provides a means for estimating a ratio of false contours in the image, and is then used to evaluate a quality mark.
In patent FR 2 785 116 filed by the applicant, the gradients calculated on the entire image to be analyzed are passed through psychovisual filters that translate the contextual masking effect. A ratio of boundaries of detected visible blocks is then calculated searching for a pseudo periodicity among high value gradients, the image quality being evaluated based on this ratio.
It is found that methods based on the calculation of gradients apply filters to only consider a certain type of image contents: boundaries or high frequencies. Therefore these methods can only be used to analyze part of information contained in the image. The result is that they have limited reliability in terms of detection of image degradations. Furthermore, methods based on the use of gradients to estimate the boundary or for the detection of contours on the image are relatively sensitive to noise, which affects the reliability of the estimate of the quality of the intrinsic content of the image.
Furthermore, methods based on the calculation of the average on the entire image drastically reduce the importance of degradations located in a part of the image, which makes it difficult to detect such local degradations and therefore affects the reliability with which the image quality is evaluated.
Some methods allow an analysis on several successive images, to reduce these disadvantages. Therefore these methods cannot be used to analyze an isolated image outside the scope of the video.